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README.md

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----
-configs:
-- config_name: default
-  data_files: "main/*.parquet"
-license: cc-by-4.0
-tags:
-- molecular dynamics
-- mlip
-- interatomic potential
-pretty_name: defected phosphorene ACS 2023
----
-### Cite this dataset  
-Kývala, L., Angeletti, A., Franchini, C., and Dellago, C. _defected phosphorene ACS 2023_. ColabFit, 2023. https://doi.org/10.60732/87b2341a
-  ### View on the ColabFit Exchange  
-https://materials.colabfit.org/id/DS_k059wtxqsksu_0  
-# Dataset  Name  
-defected phosphorene ACS 2023  
-### Description  
-This dataset contains pristine monolayer phosphorene as well as structures with monovacancies which were used to train an artificial neural network (ANN) for use with a high-dimensional neural network potentials molecular dynamics (HDNNP-MD) simulation. The publication investigates the mechanism and rates of the processes of defect diffusion, as well as monovacancy-to-divacancy defect coalescence.  
-<br>Additional details stored in dataset columns prepended with "dataset_".  
-### Dataset authors  
-Lukáš Kývala, Andrea Angeletti, Cesare Franchini, Christoph Dellago  
-### Publication  
-https://doi.org/10.1021/acs.jpcc.3c05713  
-### Original data link 
-https://doi.org/10.5281/zenodo.8421094  
-### License  
-CC-BY-4.0  
-### Number of unique molecular configurations  
-5091  
-### Number of atoms  
-722311  
-### Elements included  
-P  
-### Properties included  
-energy, atomic forces, cauchy stress